DataExample: Simulated data with one true canonical correlation vectors.

In lasandrall/SELPCCA: Sparse Canonical Correlation Analysis for Associating Mutiple High Dimensional Data

Description

Simulated data with one true canonical correlation vectors for first and second datasets. The first 20 and 15 variables are nonzero (i.e., signal variables) in the first canonical correlation vectors for the first and second datasets respectively.

Usage

data(DataExample)

Format

A list with 7 elements

Xdata1

A matrix of size 80 \times 200 for first dataset. Rows are samples and columns are variables.

Xdata2

A matrix of size 80 \times 150 for second dataset. Rows are samples and columns are variables.

Xtestdata1

A matrix of size 400 \times 200 for first dataset. Rows are samples and columns are variables.

Xtestdata2

A matrix of size 400 \times 150 for second dataset. Rows are samples and columns are variables.

TrueAlpha

The first canonical correlation vector for Xdata1.

TrueBeta

The first canonical correlation vector for Xdata2.

TrueCorr

The first canonical correlation coefficient.

References

Sandra E. Safo, Jeongyoun Ahn, Yongho Jeon, and Sungkyu Jung (2018) , Sparse Generalized Eigenvalue Problem with Application to Canonical Correlation Analysis for Integrative Analysis of Methylation and Gene Expression Data. Biometrics

Examples

#see example in multiplescca or cvselpscca

lasandrall/SELPCCA documentation built on June 8, 2020, 12:38 a.m.